Comparer des méthodes

	Recuit simulé bayésien	Chaîne de Markov Monte Carlo (MCMC)
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1984	1953 (Metropolis-Hastings); 1984 (Gibbs)
Auteur d'origine	Geman, S. & Geman, D. (Bayesian framing); Kirkpatrick, S. et al. (SA foundation)	Metropolis et al. (1953); Gibbs sampler formalised by Geman & Geman (1984)
Type	Probabilistic metaheuristic with Bayesian inference	Simulation-based Bayesian inference / numerical integration
Source fondatrice	Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680.	Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A. & Rubin, D.B. (2013). Bayesian Data Analysis (3rd ed.). Chapman & Hall/CRC.
Alias	BSA, Bayesian SA, Bayesian Stochastic Annealing, Bayesian Thermodynamic Optimization	MCMC, Metropolis-Hastings, Gibbs sampling, Markov Zinciri Monte Carlo (MCMC — Metropolis-Hastings, Gibbs)
Apparentées	5	5
Résumé	Bayesian Simulated Annealing (BSA) integrates Bayesian prior knowledge about the objective landscape into the simulated annealing search process. By encoding beliefs about promising regions as prior distributions and updating them as the search progresses, BSA focuses computational effort on high-probability areas of the solution space, accelerating convergence and improving solution quality compared to uninformed SA.	Markov Chain Monte Carlo (MCMC) is a family of simulation algorithms that constructs a Markov chain whose stationary distribution is the target posterior, enabling Bayesian inference and high-dimensional integral computation that would otherwise be analytically intractable. Pioneered by Metropolis and colleagues in 1953 and extended by Hastings in 1970, MCMC underpins modern Bayesian statistics. The two most widely used variants are Metropolis-Hastings, which proposes moves from a general proposal distribution, and Gibbs sampling, which draws each parameter in turn from its full conditional distribution.